Development of a method for assessing forecast of social impact in regional communities
DOI:
https://doi.org/10.15587/1729-4061.2021.249313Keywords:
socio-cyber-physical system, social networks, models of influence, rating of political parties, regional societyAbstract
The development of the social aspect of the world community is closely related to the expansion of the range of digital services in cyberspace. A special place in which social networks occupy. The world's leading states are conducting information operations in this environment to achieve geopolitical goals. Such processes are reflected in real social and political life. This makes it possible to influence not only the social groups of society, but also to ensure manipulation in political "games" in the conduct of hybrid wars.
The simultaneous interaction of social factors, influencing factors, the presence of communities in social networks forms a full-fledged socio-cyber-physical system capable of integrating real and virtual interactions to manage regional communities.
The article proposes a method for predicting the assessment of social mutual influence between “formal” and “informal” leaders and regional societies. The proposed models make it possible to form not only a forecast of the influence of agents, but also the interaction of various agents, taking into account their formal and informal influences, the use of administrative resources, political moods of the regional society. This approach allows dynamic modeling based on impact and relationship analysis.
The presented results of simulation modeling do not contradict the results of opinion polls and make it possible to form a set of measures that can be aimed at overcoming the negative impact on the regional society of both individual “leaders” and political parties. Analysis of the simulation results allows to increase both the political and social stability of the regional society, helps to prevent conflict moods and contradictions.
References
- Hryshchuk, R. V., Danyk, Yu. H. (2016). Osnovy kibernetychnoi bezpeky. Zhytomyr: ZhNAEU, 636.
- Xia, F., Ma, J. (2011). Building smart communities with cyber-physical systems. Proceedings of 1st International Symposium on From Digital Footprints to Social and Community Intelligence - SCI ’11. doi: https://doi.org/10.1145/2030066.2030068
- Guo, B., Yu, Z., Zhou, X. (2015). A Data-Centric Framework for Cyber-Physical-Social Systems. IT Professional, 17 (6), 4–7. doi: https://doi.org/10.1109/mitp.2015.116
- Kuang, L., Yang, L. T., Liao, Y. (2020). An Integration Framework on Cloud for Cyber-Physical-Social Systems Big Data. IEEE Transactions on Cloud Computing, 8 (2), 363–374. doi: https://doi.org/10.1109/tcc.2015.2511766
- Lin, C.-C., Deng, D.-J., Jhong, S.-Y. (2020). A Triangular NodeTrix Visualization Interface for Overlapping Social Community Structures of Cyber-Physical-Social Systems in Smart Factories. IEEE Transactions on Emerging Topics in Computing, 8 (1), 58–68. doi: https://doi.org/10.1109/tetc.2017.2671846
- De, S., Zhou, Y., Larizgoitia Abad, I., Moessner, K. (2017). Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey. Applied Sciences, 7 (10), 1017. doi: https://doi.org/10.3390/app7101017
- Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489 (7415), 295–298. doi: https://doi.org/10.1038/nature11421
- Jayles, B., Kim, H., Escobedo, R., Cezera, S., Blanchet, A., Kameda, T. et. al. (2017). How social information can improve estimation accuracy in human groups. Proceedings of the National Academy of Sciences, 114 (47), 12620–12625. doi: https://doi.org/10.1073/pnas.1703695114
- Almaatouq, A., Noriega-Campero, A., Alotaibi, A., Krafft, P. M., Moussaid, M., Pentland, A. (2020). Adaptive social networks promote the wisdom of crowds. Proceedings of the National Academy of Sciences, 117 (21), 11379–11386. doi: https://doi.org/10.1073/pnas.1917687117
- Phoa, F. K. H., Weng, P. C.-Y., Chiang, Y.-S. (2016). A mathematical model on the propagation of node attributes on a social network. IAENG Transactions on Engineering Sciences. doi: https://doi.org/10.1142/9789813142725_0009
- Kao, A. B., Berdahl, A. M., Hartnett, A. T., Lutz, M. J., Bak-Coleman, J. B., Ioannou, C. C. et. al. (2018). Counteracting estimation bias and social influence to improve the wisdom of crowds. Journal of The Royal Society Interface, 15 (141), 20180130. doi: https://doi.org/10.1098/rsif.2018.0130
- Lorenz, J., Rauhut, H., Schweitzer, F., Helbing, D. (2011). How social influence can undermine the wisdom of crowd effect. Proceedings of the National Academy of Sciences, 108 (22), 9020–9025. doi: https://doi.org/10.1073/pnas.1008636108
- Jayles, B., Sire, C., Kurvers, R. H. J. M. (2021). Impact of sharing full versus averaged social information on social influence and estimation accuracy. Journal of The Royal Society Interface, 18 (180), 20210231. doi: https://doi.org/10.1098/rsif.2021.0231
- Madirolas, G., de Polavieja, G. G. (2015). Improving Collective Estimations Using Resistance to Social Influence. PLOS Computational Biology, 11 (11), e1004594. doi: https://doi.org/10.1371/journal.pcbi.1004594
- Parthasarathy, S., Ruan, Y., Satuluri, V. (2011). Community Discovery in Social Networks: Applications, Methods and Emerging Trends. Social Network Data Analytics, 79–113. doi: https://doi.org/10.1007/978-1-4419-8462-3_4
- Sun, J., Tang, J. (2011). A Survey of Models and Algorithms for Social Influence Analysis. Social Network Data Analytics, 177–214. doi: https://doi.org/10.1007/978-1-4419-8462-3_7
- Anagnostopoulos, A., Kumar, R., Mahdian, M. (2008). Influence and correlation in social networks. Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 08. doi: https://doi.org/10.1145/1401890.1401897
- Goyal, A., Bonchi, F., Lakshmanan, L. V. S. (2010). Learning influence probabilities in social networks. Proceedings of the Third ACM International Conference on Web Search and Data Mining - WSDM ’10. doi: https://doi.org/10.1145/1718487.1718518
- Xiang, R., Neville, J., Rogati, M. (2010). Modeling relationship strength in online social networks. Proceedings of the 19th International Conference on World Wide Web - WWW ’10. doi: https://doi.org/10.1145/1772690.1772790
- Scripps, J., Tan, P.-N., Esfahanian, A.-H. (2009). Measuring the effects of preprocessing decisions and network forces in dynamic network analysis. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’09. doi: https://doi.org/10.1145/1557019.1557102
- Tang, L., Liu, H. (2009). Relational learning via latent social dimensions. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’09. doi: https://doi.org/10.1145/1557019.1557109
- Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O., Korol, O., Milevskyi, S. et. al. (2021). Synergy of building cybersecurity systems. Kharkiv: PC TECHNOLOGY CENTER, 188. doi: https://doi.org/10.15587/978-617-7319-31-2
- Pozacherhovi vybory narodnykh deputativ Ukrainy 21 lypnia 2019 roku. Ofitsiynyi sait «Tsentralnoi vyborchoi komisii Ukrainy». Available at: https://www.cvk.gov.ua/vibory_category/vibori-narodnih-deputativ-ukraini/pozachergovi-vibori-narodnih-deputativ-ukraini-21-lipnya-2019-roku.html
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Copyright (c) 2021 Serhii Yevseiev, Yurii Ryabukha, Oleksandr Milov, Stanislav Milevskyi, Serhii Pohasii, Yevgen Melenti, Yevheniia Ivanchenko, Ihor Ivanchenko, Ivan Opirskyy, Igor Pasko
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